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Connecting digital and physical representations through semantics and geometry
KTH, Skolan för arkitektur och samhällsbyggnad (ABE), Fastigheter och byggande, Geodesi och satellitpositionering.ORCID-id: 0000-0001-9032-4305
2019 (engelsk)Licentiatavhandling, med artikler (Annet vitenskapelig)
Abstract [en]

The fields of geodesy and building information modeling (BIM) meet each other in the intersection between the physical and the digital world. Within the construction industry, the role of geodesy has typically been to describe the position of assets and to transform the geometries of those assets between coordinate systems suitable for design and coordinate systems with a known relation to the Earth. This is not changed by the introduction of BIM but rather emphasized by it, as higher degrees of automation and prefabrication increases the need for strict and non-distorting transformations. The objectoriented aspects of BIM require that captured geodata can be semantically classified and that objects can be reconstructed and extracted from the geodata. In this landscape, geodesy is the bridge between model and reality, connecting the two worlds through both semantics and geometry. This thesis is a comprehensive summary of three papers within these two topics. The first paper describes the geometric transformations required throughout the life cycle of a built asset and assesses the georeferencing capabilities of the open BIM standard Industry Foundation Classes (IFC). The second and third paper propose and showcase a methodology where image-based deep learning is used to extract roadside objects from mobile mapping data. The findings of the first paper include suggestions for how IFC can be improved in order to facilitate better georeferencing, and the second and third paper show that the proposed methodology performs well in comparison to a manual classification.

Abstract [sv]

De två områdena geodesi och byggnadsinformationsmodellering (BIM) möter varandra i skärningspunkten mellan den fysiska och den digitala världen. Inom byggindustrin har geodesins roll historiskt varit att positionsbestämma anläggningar samt att transformera deras geometrier mellan koordinatsystem lämpliga antingen för design eller för inmätning och utsättning. Detta har inte ändrats av att BIM börjat användas, utan det har snarare blivit ännu viktigare då högre nivåer av automatisering och prefabricering ställer högre krav på strikta och icke-deformerande transformationer. De objektorienterade aspekterna av BIM kräver att infångade geodata kan klassificeras semantiskt och att objekt kan återskapas och extraheras från dessa geodata. I detta landskap utgör geodesin en bro mellan modell och verklighet, och sammanlänkar dessa världar genom både semantik och geometri. Denna avhandling är en sammanfattning av tre artiklar inom dessa två områden. Den första artikeln beskriver de geometriska transformationer som krävs genom en anläggnings livscykel och utvärderar georefereringsförmågan hos den öppna BIM-standarden Industry Foundation Classes (IFC). Den andra och tredje artikeln föreslår och demonstrerar en metod där bildbaserad deep learning används för att extrahera vägnära objekt ur data insamlat genom mobile mapping. Slutsatserna från den första artikeln inkluderar förslag på hur IFC kan utvecklas för att möjliggöra bättre georeferering, och de två andra artiklarna visar att den föreslagna metoden presterar väl i jämförelse med en manuell klassificering.

sted, utgiver, år, opplag, sider
Stockholm: KTH Royal Institute of Technology, 2019. , s. 32
Serie
TRITA-ABE-DLT ; 1914
HSV kategori
Forskningsprogram
Geodesi och geoinformatik
Identifikatorer
URN: urn:nbn:se:kth:diva-250333ISBN: 978-91-7873-196-1 (tryckt)OAI: oai:DiVA.org:kth-250333DiVA, id: diva2:1307828
Presentation
2019-05-23, V3, Teknikringen 72, KTH, Stockholm, 10:00 (svensk)
Opponent
Veileder
Merknad

QC 20190429

Tilgjengelig fra: 2019-04-29 Laget: 2019-04-29 Sist oppdatert: 2019-04-29bibliografisk kontrollert
Delarbeid
1. Geographic capabilities and limitations of Industry Foundation Classes
Åpne denne publikasjonen i ny fane eller vindu >>Geographic capabilities and limitations of Industry Foundation Classes
2018 (engelsk)Inngår i: Automation in Construction, ISSN 0926-5805, E-ISSN 1872-7891, Vol. 96, s. 554-566Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

Infrastructure design is conducted in a 3D Cartesian coordinate system with the assumption that the Earth is flat and that the scale is constant over the entire project area. Map projections are commonly used to georeference the designed geometries before constructing them on the surface of the Earth. The scale in a map projection varies depending on the position in the map plane, which leads to scale distortions between the designed geometries and the geometries staked out for construction. These distortions are exaggerated for large longitudinal projects such as the construction of roads and railroads because the construction site spans a larger area. Building Information Modeling (BIM) is increasing in popularity as a way to manage information within a construction project. Its use is more widespread in the building industry, but it is currently being adopted by the infrastructure industry as well. The open BIM standard IFC (Industry Foundation Classes) has recently developed support for alignment geometries, and full support for disciplines such as road and railroad construction is underway. This study tests whether the current IFC standard can facilitate georeferencing with sufficiently low distortion for the construction of infrastructure. This is done by performing georeferencing using three different methods, all using the information provided in the IFC schema, and by calculating the scale distortions caused by the different methods. It is concluded that the geographic capabilities of the IFC schema could be improved by adding a separate scale factor for the horizontal plane and support for object-specific map projections.

sted, utgiver, år, opplag, sider
Elsevier, 2018
Emneord
Georeferencing, BIM, IFC
HSV kategori
Identifikatorer
urn:nbn:se:kth:diva-240760 (URN)10.1016/j.autcon.2018.10.014 (DOI)000452345800042 ()2-s2.0-85055585349 (Scopus ID)
Forskningsfinansiär
Swedish Transport Administration, FUD 6240 FUD 6240
Merknad

QC 20190107

Tilgjengelig fra: 2019-01-07 Laget: 2019-01-07 Sist oppdatert: 2019-04-29bibliografisk kontrollert
2. Classification and object reconstruction in point clouds using semantic segmentation and transfer learning
Åpne denne publikasjonen i ny fane eller vindu >>Classification and object reconstruction in point clouds using semantic segmentation and transfer learning
2019 (engelsk)Konferansepaper, Publicerat paper (Fagfellevurdert)
HSV kategori
Identifikatorer
urn:nbn:se:kth:diva-250331 (URN)
Konferanse
CIB World Building Congress 2019
Merknad

QC 20190429

Tilgjengelig fra: 2019-04-29 Laget: 2019-04-29 Sist oppdatert: 2019-04-29bibliografisk kontrollert
3. Automatic extraction of roadside objects from mobile mapping data
Åpne denne publikasjonen i ny fane eller vindu >>Automatic extraction of roadside objects from mobile mapping data
2019 (engelsk)Inngår i: Artikkel i tidsskrift (Fagfellevurdert) Submitted
HSV kategori
Identifikatorer
urn:nbn:se:kth:diva-250332 (URN)
Merknad

QC 20190429

Tilgjengelig fra: 2019-04-29 Laget: 2019-04-29 Sist oppdatert: 2019-04-29bibliografisk kontrollert

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